1. 1 GALORE update: time and location services in a tiered sensor architecture Naim Busek {ndbusek@lecs.cs.ucla.edu} Vladimir Bychkovskiy {vladimir@lecs.cs.ucla.edu} Jeremy Elson {jelson@lecs.cs.ucla.edu}{jelson@lecs.cs.ucla.edu Lewis Girod {girod@lecs.cs.ucla.edu}{girod@lecs.cs.ucla.edu Advisor: Deborah Estrin {destrin@cs.ucla.edu} NEST PI Meeting July 2002

2. 2 System Design Motes:  Periodically emit coded acoustic “chirp s” (511 bits) IPAQs:  Listen for chirps (buffer time series - mote can’t do this)  run matched filter  Share ranges with each other via 802.11; trilaterate  IPAQs currently configured with their position; future: range to each other; self-configure Time sync  Allows computation of acoustic time-of-flight  One IPAQ has a “MoteNIC” to sync mote and IPAQ domains Compaq IPAQ Model 3760 Modified motes have audio amplifiers and a speaker

3. 3 Inter and Intra Node Sync 4 timesync modalities:  2 RBS (mote to mote, IPAQ to IPAQ)  2 intra-node (IPAQ to motenic, IPAQ to codec) CPU 1 802.11 IPAQ 802.11Broadcaster CPU 2 802.11 CODEC 1CODEC 2 Convert to CPU1 (RMS 0uS) Convert to CPU1 (RMS 1uS) Convert to Mote1 (RMS 10uS) Convert to CPU2 (RMS 0uS) 1 These results from “Rene” Mote using slower radio (53uS bit time) and approximate bit timing. Reported results from the “Mica” stack (20uS bits time) indicate 0.25uS packet timing. Acoustic Pulse Acoustic Mote Mote 1 IPAQ 1 IPAQ 2 Mote Broadcaster Convert to CPU1 (RMS 0uS) Convert to CPU1 (RMS 2uS)

4. 4 Time Mote to Mote Timesync: 10 usec RMS

5. 5 Positioning Algorithms Positioning algorithm  Converts range data into consistent coordinate system  So far, positioning algorithm developed in simulation IPAQ specific domain knowledge  Speaker and microphone not co-located  Therefore ranges are not always equal to “reverse range” Acoustics-specific domain knowledge  Acoustic error model  Major component of error is excess path length caused by non-line-of-sight ranges  Current implementation uses customized mass-spring model to compensate for NLOS

6. 6 Results of Positioning Algorithm on Simulated Data RMS Position error = 2.8cm RMS Range error = 2.5cm Input noise: 2cm variance quantized to 0.71cm 10% NLOS, uniform 0-40cm excess path, uncorrelated cm